code: def setfit_trainer():
      dataset = load_dataset("sst2")
  
      train_dataset = sample_dataset(
          dataset["train"], label_column="label", num_samples=8
      )
      eval_dataset = dataset["validation"]
  
      model = SetFitModel.from_pretrained(
          "sentence-transformers/all-MiniLM-L6-v2"
      )
  
      return SetFitTrainer(
          model=model,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          metric="accuracy",
          column_mapping={"sentence": "text", "label": "label"},
          args=SetFitTrainingArguments(
              loss=CosineSimilarityLoss,
              batch_size=16,
              num_iterations=5,
              num_epochs=1,
              report_to="none",
          ),
      )
  
imports: import random
  import numpy
  import optuna
  import pytest
  import sklearn
  import sklearn.cluster
  import torch
  from datasets import load_dataset
  from sentence_transformers.losses import CosineSimilarityLoss
  from setfit import SetFitModel, sample_dataset
  from setfit import Trainer
  from setfit import TrainingArguments
  from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, Trainer, TrainingArguments, pipeline
  import mlflow
label_desc: Use image node_id 2 for calling a global function with example usage: setfit_trainer() and returns: SetFitTrainer